In cyber-physical systems (CPSs) that interact between physical and information components, there are many sensors that are connected through a communication network. In such cases, the reduction of communication costs is important. Event-triggered control that the control input is updated only when the measured value is widely changed is well known as one of the control methods of CPSs. In this paper, we propose a design method of output feedback controllers with decentralized event-triggering mechanisms, where the notion of uniformly ultimate boundedness is utilized as a control specification. Using this notion, we can guarantee that the state stays within a certain set containing the origin after a certain time, which depends on the initial state. As a result, the number of times that the event occurs can be decreased. First, the design problem is formulated. Next, this problem is reduced to a BMI (bilinear matrix inequality) optimization problem, which can be solved by solving multiple LMI (linear matrix inequality) optimization problems. Finally, the effectiveness of the proposed method is presented by a numerical example.

Information-theoretic lower bounds of the Bayes risk have been investigated for a problem of parameter estimation in a Bayesian setting. Previous studies have proven the lower bound of the Bayes risk in a different manner and characterized the lower bound via different quantities such as mutual information, Sibson's α-mutual information, f-divergence, and Csiszár's f-informativity. In this paper, we introduce an inequality called a “meta-bound for lower bounds of the Bayes risk” and show that the previous results can be derived from this inequality.

Identity-based encryption with equality test (IBEET) is a variant of identity-based encryption (IBE), in which any user with trapdoors can check whether two ciphertexts are encryption of the same plaintext. Although several lattice-based IBEET schemes have been proposed, they have drawbacks in either security or efficiency. Specifically, most IBEET schemes only satisfy selective security, while public keys of adaptively secure schemes in the standard model consist of matrices whose numbers are linear in the security parameter. In other words, known lattice-based IBEET schemes perform poorly compared to the state-of-the-art lattice-based IBE schemes (without equality test). In this paper, we propose a semi-generic construction of CCA-secure lattice-based IBEET from a certain class of lattice-based IBE schemes. As a result, we obtain the first lattice-based IBEET schemes with adaptive security and CCA security in the standard model without sacrificing efficiency. This is because, our semi-generic construction can use several state-of-the-art lattice-based IBE schemes as underlying schemes, e.g. Yamada's IBE scheme (CRYPTO'17).

Distance-aware quality adaptation is a potential approach to reduce the resource requirement for the transmission and rendering of textured 3D meshes. In this paper, we carry out a subjective experiment to investigate the effects of the distance from the camera on the perceptual quality of textured 3D meshes. Besides, we evaluate the effectiveness of eight image-based objective quality metrics in representing the user's perceptual quality. Our study found that the perceptual quality in terms of mean opinion score increases as the distance from the camera increases. In addition, it is shown that normalized mutual information (NMI), a full-reference objective quality metric, is highly correlated with subjective scores.

In this paper, the quality and transferred data based video bitrate control method for web-conferencing services is proposed, aiming to reduce transferred data by suppressing excessive quality. In web-conferencing services, the video bitrate is generally controlled in accordance with the network conditions (e.g., jitter and packet loss rate) to improve users' quality. However, in such a control, the bitrate is excessively high when the network conditions is sufficiently high (e.g., high throughput and low jitter), which causes an increased transferred data volume. The increased volume of data transferred leads to increased operational costs, such as network costs for service providers. To solve this problem, we developed a method to control the video bitrate of each user to achieve the required quality determined by the service provider. This method is implemented in an actual web-conferencing system and evaluated under various conditions. It was shown that the bitrate could be controlled in accordance with the required quality to reduce the transferred data volume.

It has become increasingly important for industries to promote digital transformation by utilizing 5G and industrial internet of things (IIoT) to improve productivity. To protect IIoT application performance (work speed, productivity, etc.), it is often necessary to satisfy quality of service (QoS) requirements precisely. For this purpose, there is an increasing need to automatically identify the root causes of radio-quality deterioration in order to take prompt measures when the QoS deteriorates. In this paper, a method for identifying the root cause of 5G radio-quality deterioration is proposed that uses machine learning. This Random Forest based method detects the root cause, such as distance attenuation, shielding, fading, or their combination, by analyzing the coefficients of a quadratic polynomial approximation in addition to the mean values of time-series data of radio quality indicators. The detection accuracy of the proposed method was evaluated in a simulation using the MATLAB 5G Toolbox. The detection accuracy of the proposed method was found to be 98.30% when any of the root causes occurs independently, and 83.13% when the multiple root causes occur simultaneously. The proposed method was compared with deep-learning methods, including bidirectional long short-term memory (bidirectional-LSTM) or one-dimensional convolutional neural network (1D-CNN), that directly analyze the time-series data of the radio quality, and the proposed method was found to be more accurate than those methods.

Non-orthogonal multipe access based multiple-input multiple-output system (MIMO-NOMA) has been widely used in improving user's achievable rate of millimeter wave (mmWave) communication. To meet different requirements of each user in multi-user beams, this paper proposes a power allocation algorithm to satisfy the quality of service (QoS) of head user while maximizing the minimum rate of edge users from the perspective of max-min fairness. Suppose that the user who is closest to the base station (BS) is the head user and the other users are the edge users in each beam in this paper. Then, an optimization problem model of max-min fairness criterion is developed under the constraints of users' minimum rate requirements and the total transmitting power of the BS. The bisection method and Karush-Kuhn-Tucher (KKT) conditions are used to solve this complex non-convex problem, and simulation results show that both the minimum achievable rates of edge users and the average rate of all users are greatly improved significantly compared with the traditional MIMO-NOMA, which only consider max-min fairness of users.

In this paper, we apply two methods in machine learning, dropout and semi-supervised learning, to a recently proposed method called CSQ-SDL which uses deep neural networks for evaluating shift quality from time-series measurement data. When developing a new Automatic Transmission (AT), calibration takes place where many parameters of the AT are adjusted to realize pleasant driving experience in all situations that occur on all roads around the world. Calibration requires an expert to visually assess the shift quality from the time-series measurement data of the experiments each time the parameters are changed, which is iterative and time-consuming. The CSQ-SDL was developed to shorten time consumed by the visual assessment, and its effectiveness depends on acquiring a sufficient number of data points. In practice, however, data amounts are often insufficient. The methods proposed here can handle such cases. For the cases wherein only a small number of labeled data points is available, we propose a method that uses dropout. For those cases wherein the number of labeled data points is small but the number of unlabeled data is sufficient, we propose a method that uses semi-supervised learning. Experiments show that while the former gives moderate improvement, the latter offers a significant performance improvement.

We propose a non-photorealistic rendering method for automatically generating point-light-style (PLS) images from photographic images using peripheral difference filters with different window sizes. The proposed method can express PLS patterns near the edges of photographic images as dots. To verify the effectiveness of the proposed method, experiments were conducted to visually confirm PLS images generated from various photographic images.

The Common Media Application Format (CMAF) is a standard for adaptive bitrate live streaming. The CMAF adapts chunk encoding and enables low-latency live streaming. However, conventional bandwidth estimation for adaptive bitrate streaming underestimates bandwidth because download time is affected not only by network bandwidth but also by the idle times between chunks in the same segment. Inaccurate bandwidth estimation decreases the quality of experience of the streaming client. In this paper, we propose a chunk-grouping method to estimate the available bandwidth for adaptive bitrate live streaming. In the proposed method, by delaying HTTP request transmission and bandwidth estimation using grouped chunks, the client estimates the available bandwidth accurately due to there being no idle times in the grouped chunks. In addition, we extend the proposed method to dynamically change the number of grouping chunks according to buffer length during downloading of the previous segment. We evaluate the proposed methods under various network conditions in order to confirm the effectiveness of the proposed methods.

This letter presents a global feature-based method for evaluating the no reference quality of scanning electron microscopy (SEM) contrast-distorted images. Based on the characteristics of SEM images and the human visual system, the global features of SEM images are extracted as the score for evaluating image quality. In this letter, the texture information of SEM images is first extracted using a low-pass filter with orientation, and the amount of information in the texture part is calculated based on the entropy reflecting the complexity of the texture. The singular values with four scales of the original image are then calculated, and the amount of structural change between different scales is calculated and averaged. Finally, the amounts of texture information and structural change are pooled to generate the final quality score of the SEM image. Experimental results show that the method can effectively evaluate the quality of SEM contrast-distorted images.

With the rapid increase in the amount of data communication in 5G networks, there is a strong demand to reduce the power of the entire network, so the use of highly power-efficient millimeter-wave (mm-wave) networks is being considered. However, while mm-wave communication has high power efficiency, it has strong straightness, so it is difficult to secure stable communication in an environment with blocking. Especially when considering use cases such as autonomous driving, continuous communication is required when transmitting streaming data such as moving images taken by vehicles, it is necessary to compensate the blocking problem. For this reason, the authors examined an optimum radio access technology (RAT) selection scheme which selects mm-wave communication when mm-wave can be used and select wide-area macro-communication when mm-wave may be blocked. In addition, the authors implemented the scheme on a prototype device and conducted field tests and confirmed that mm-wave communication and macro communication were switched at an appropriate timing.

We propose Quality Enhancement via a Side bitstream Network (QESN) technique for lossy image compression. The proposed QESN utilizes the network architecture of deep image compression to produce a bitstream for enhancing the quality of conventional compression. We also present a loss function that directly optimizes the Bjontegaard delta bit rate (BD-BR) by using a differentiable model of a rate-distortion curve. Experimental results show that QESN improves the rate by 16.7% in the BD-BR compared to Better Portable Graphics.

In this paper, a metadata-based quality-estimation model is proposed for tile-based omnidirectional video streaming services, aiming to realize quality monitoring during service provision. In the tile-based omnidirectional video (ODV) streaming services, the ODV is divided into tiles, and the high-quality tiles and the low-quality tiles are distributed in accordance with the user's viewing direction. When the user changes the viewing direction, the user temporarily watches video with the low-quality tiles. In addition, the longer the time (delay time) until the high-quality tile for the new viewing direction is downloaded, the longer the viewing time of video with the low-quality tile, and thus the delay time affects quality. From the above, the video quality of the low-quality tiles and the delay time significantly impact quality, and these factors need to be considered in the quality-estimation model. We develop quality-estimation models by extending the conventional quality-estimation models for 2D adaptive streaming. We also show that the quality-estimation model using the bitrate, resolution, and frame rate of high- and low-quality tiles and that the delay time has sufficient estimation accuracy based on the results of subjective quality evaluation experiments.

Identity-based encryption with equality test (IBEET) is a generalization of the traditional identity-based encryption (IBE) and public key searchable encryption, where trapdoors enable users to check whether two ciphertexts of distinct identities are encryptions of the same plaintext. By definition, IBEET cannot achieve indistinguishability security against insiders, i.e., users who have trapdoors. To address this issue, IBEET against insider attacks (IBEETIA) was later introduced as a dual primitive. While all users of IBEETIA are able to check whether two ciphertexts are encryptions of the same plaintext, only users who have tokens are able to encrypt plaintexts. Hence, IBEETIA is able to achieve indistinguishability security. On the other hand, the definition of IBEETIA weakens the notion of IBE due to its encryption inability. Nevertheless, known schemes of IBEETIA made use of rich algebraic structures such as bilinear groups and lattices. In this paper, we propose a generic construction of IBEETIA without resorting to rich algebraic structures. In particular, the only building blocks of the proposed construction are symmetric key encryption and pseudo-random permutations in the standard model. If a symmetric key encryption scheme satisfies CCA security, our proposed IBEETIA scheme also satisfies CCA security.

Approximate computing (AC) has recently emerged as a promising approach to the energy-efficient design of digital systems. For realizing the practical AC design, we need to verify whether the designed circuit can operate correctly under various operating conditions. Namely, the verification needs to efficiently find fatal logic errors or timing errors that violate the constraint of computational quality. This work focuses on the verification where the computational results can be observed, the computational quality can be calculated from computational results, and the constraint of computational quality is given and defined as the constraint which is set to the computational quality of designed AC circuit with given workloads. Then, this paper proposes a novel dynamic verification framework of the AC circuit. The key idea of the proposed framework is to incorporate a quality assessment capability into the Coverage-based Grey-box Fuzzing (CGF). CGF is one of the most promising techniques in the research field of software security testing. By repeating (1) mutation of test patterns, (2) execution of the program under test (PUT), and (3) aggregation of coverage information and feedback to the next test pattern generation, CGF can explore the verification space quickly and automatically. On the other hand, CGF originally cannot consider the computational quality by itself. For overcoming this quality unawareness in CGF, the proposed framework additionally embeds the Design Under Verification (DUV) component into the calculation part of computational quality. Thanks to the DUV integration, the proposed framework realizes the quality-aware feedback loop in CGF and thus quickly enhances the verification coverage for test patterns that violate the quality constraint. In this work, we quantitatively compared the verification coverage of the approximate arithmetic circuits between the proposed framework and the random test. In a case study of an approximate multiply-accumulate (MAC) unit, we experimentally confirmed that the proposed framework achieved 3.85 to 10.36 times higher coverage than the random test.

In this paper, the spurious modes and quality-factor (Q) values of the one-port dual-mode AlN lamb-wave resonators at 500-1000 MHz were studied by theoretical analysis and experimental verification. Through finite element analysis, we found that optimizing the width of the lateral reflection boundary at both ends of the resonator to reach the quarter wavelength (λ/4), which can improve its spectral purity and shift its resonant frequency. The designed resonators were micro-fabricated by using lithography processes on a 6-inch wafer. The measured results show that the spurious mode can be converted and dissipated, splitting into several longitudinal modes by optimizing the width of the lateral reflection boundary, which are consistent well with the theoretical analysis. Similarly, optimizing the interdigital transducer (IDT) width and number of IDT fingers can also suppress the resonator's spurious modes. In addition, it is found that there is no significant difference in the Qs value for the two modes of the dual-mode resonator with the narrow anchor and full anchor. The acoustic wave leaked from the anchor into the substrate produces a small displacement, and the energy is limited in the resonator. Compared to the resonator with Au IDTs, the resonator with Al IDTs can achieve a higher Q value due to its lower thermo-elastic damping loss. The measured results show the optimized dual-mode lamb-wave resonator can obtain Qs value of 2946.3 and 2881.4 at 730.6 MHz and 859.5 MHz, Qp values of 632.5 and 1407.6, effective electromechanical coupling coefficient (k2eff) of 0.73% and 0.11% respectively, and has excellent spectral purity simultaneously.

For a long time, the compensation effect of hearing aid is mainly evaluated subjectively, and there are fewer studies of objective evaluation. Furthermore, a pure speech signal is generally required as a reference in the existing objective evaluation methods, which restricts the practicality in a real-world environment. Therefore, this paper presents a non-intrusive speech quality evaluation method for hearing aid, which combines the audiogram and weighted frequency information. The proposed model mainly includes an audiogram information extraction network, a frequency information extraction network, and a quality score mapping network. The audiogram is the input of the audiogram information extraction network, which helps the system capture the information related to hearing loss. In addition, the low-frequency bands of speech contain loudness information and the medium and high-frequency components contribute to semantic comprehension. The information of two frequency bands is input to the frequency information extraction network to obtain time-frequency information. When obtaining the high-level features of different frequency bands and audiograms, they are fused into two groups of tensors that distinguish the information of different frequency bands and used as the input of the attention layer to calculate the corresponding weight distribution. Finally, a dense layer is employed to predict the score of speech quality. The experimental results show that it is reasonable to combine the audiogram and the weight of the information from two frequency bands, which can effectively realize the evaluation of the speech quality of the hearing aid.

Machine-to-Machine (M2M) communication plays a pivotal role in the evolution of Internet of Things (IoT). Cellular networks are considered to be a key enabler for M2M communications, which are originally designed mainly for Human-to-Human (H2H) communications. The introduction of M2M users will cause a series of problems to traditional H2H users, i.e., interference between various traffic. Resource allocation is an effective solution to these problems. In this paper, we consider a shared resource block (RB) and power allocation in an H2H/M2M coexistence scenario, where M2M users are subdivided into delay-tolerant and delay-sensitive types. We first model the RB-power allocation problem as maximization of capacity under Quality-of-Service (QoS) constraints of different types of traffic. Then, a learning framework is introduced, wherein a complex agent is built from simpler subagents, which provides the basis for distributed deployment scheme. Further, we proposed distributed Q-learning based autonomous RB-power allocation algorithm (DQ-ARPA), which enables the machine type network gateways (MTCG) as agents to learn the wireless environment and choose the RB-power autonomously to maximize M2M pairs' capacity while ensuring the QoS requirements of critical services. Simulation results indicates that with an appropriate reward design, our proposed scheme succeeds in reducing the impact of delay-tolerant machine type users on critical services in terms of SINR thresholds and outage ratios.

With the popularity of smart devices, mobile crowdsensing, in which the crowdsensing platform gathers useful data from users of smart devices, e.g., smartphones, has become a prevalent paradigm. Various incentive mechanisms have been extensively adopted for the crowdsensing platform to incentivize users of smart devices to offer sensing data. Existing works have concentrated on rewarding smart-device users for their short term effort to provide data without considering the long-term factors of smart-device users and the quality of data. Our previous work has considered the quality of data of smart-device users by incorporating the long-term reputation of smart-device users. However, our previous work only considered a quality maximization problem with budget constraints on one location. In this paper, multiple locations are considered. Stackelberg game is utilized to solve a two-stage optimization problem. In the first stage, the crowdsensing platform allocates the budget to different locations and sets price as incentives for users to maximize the total data quality. In the second stage, the users make efforts to provide data to maximize its utility. Extensive numerical simulations are conducted to evaluate proposed algorithm.